• 정보보호학회논문지
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• 제13권6호
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• pp.149-161
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• 2003

부분은닉서명은 서명자가 은닉서명을 발행할 때 그가 삽입하기를 원하는 어떠한 정보를 서명에 삽입할 수 있도록 하는 암호방식으로 전자화폐나 전자투표 등 주로 행위자의 행동이 노출되어서는 안되는 보안서비스에 중요하게 활용되며 전자화폐시스템에서 은행의 데이터베이스의 무제한적인 증가문제도 해결할 수 있는 암호방식이다. 본 논문에서는 GDH군에서의 ID 기반의 효율적인 부분은닉서명 방식을 제안한다. 제안한 방식은 Weil-pairing과 같은 bilinear 함수를 사용하며 CDHP의 어려움에 기반을 두고 있으며 기존의 부분은닉서명에 비하여 통신량, 통신횟수, 연산량을 줄여 무선 환경에 적용할 수 있는 효율적인 서명방식이다.

• Smart Structures and Systems
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• 제28권6호
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• pp.745-760
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• 2021

Dynamic buckling of structure is one of the failure modes that needs to be considered since it may result in catastrophic failure of the structure in a short period of time. For a thin fiber-reinforced polymer (FRP) plate under compression, buckling is an inherent hazard which will be intensified by the existence of defects like holes, cracks, and delamination. On the other hand, the growth of the delamination is another prime concern for thin FRP plates. In the current paper, reinforcing the plates against buckling is realized by using SMA wires in the form of stitches. A numerical framework is proposed to simulate the dynamic instability emphasizing the effect of the SMA stitches in suppressing delamination growth. The suggested algorithm is more accurate than the other methods when considering the transformation point of the SMA wires and the modeling of the cohesive zone using simple and yet reliable technique. The computational design of the method by producing the line by line orders leads to a simple algorithm for simulating the super-elastic behavior. The Lagoudas constitutive model of the SMA material is implemented in the form of user material subroutines (VUMAT). The normal bilinear spring model is used to reproduce the cohesive zone behavior. The nonlinear finite element formulation is programmed into FORTRAN using the Newmark-beta numerical time-integration approach. The obtained results are compared with the results obtained by the finite element method using ABAQUS/Explicit solver. The obtained results by the proposed algorithm and those by ABAQUS are in good agreement.

• 한국정보통신학회논문지
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• 제26권2호
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• pp.181-186
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• 2022

포아즌 볼츠만 방정식 (Poisson-Boltzmann equation, PBE)은 생물물리, 콜로이드 화학 등에서 등장하는 문제들을 모델링하는데 사용되는 방정식이다. 따라서 PBE의 수치해를 효율적으로 예측하는 것은 중요한 이슈이다. 저자들은 기존의 연구에서 PBE를 풀기위한 딥러닝 방법을 제안하였으나, 딥러닝을 훈련하기 위한 샘플을 생성하는 시간이 컸다는 어려움이 있었다. 본 논문에서는 FEM 수치해를 생성하는데 걸리는 시간을 줄이는 두가지 방안을 마련하였다. 첫째로 대수 방정식을 만들 때 bilinar form에 포함되는 penalty 파라메터를 실험적으로 조정하였다. 두 번째로, 대수적멀티그리드 기법을 활용하여 대수 방정식의 컨디션 넘버를 meshsize와 무관하게 만들었다. 따라서 PBE 방정식의 대수 방정식을 풀 때 계산 시간을 효과적으로 줄였다. 이러한 대수적 멀티그리드를 사용한 방법은 다양한 분야에서 딥러닝의 샘플을 생성하는데 효과적으로 활용될 수 있을 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.998-1019
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• 2024

Mobile cloud computing is a very attractive service paradigm that outsources users' data computing and storage from mobile devices to cloud data centers. To protect data privacy, users often encrypt their data to ensure data sharing securely before data outsourcing. However, the bilinear and power operations involved in the encryption and decryption computation make it impossible for mobile devices with weak computational power and network transmission capability to correctly obtain decryption results. To this end, this paper proposes an outsourcing decryption algorithm of verifiable transformed ciphertext. First, the algorithm uses the key blinding technique to divide the user's private key into two parts, i.e., the authorization key and the decryption secret key. Then, the cloud data center performs the outsourcing decryption operation of the encrypted data to achieve partial decryption of the encrypted data after obtaining the authorization key and the user's outsourced decryption request. The verifiable random function is used to prevent the semi-trusted cloud data center from not performing the outsourcing decryption operation as required so that the verifiability of the outsourcing decryption is satisfied. Finally, the algorithm uses the authorization period to control the final decryption of the authorized user. Theoretical and experimental analyses show that the proposed algorithm reduces the computational overhead of ciphertext decryption while ensuring the verifiability of outsourcing decryption.

• Earthquakes and Structures
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• 제12권4호
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• pp.397-407
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• 2017

To estimate the structural seismic demand, some methods are based on an equivalent linear system such as the Capacity Spectrum Method, the N2 method and the Equivalent Linearization method. Another category, widely investigated, is based on displacement correction such as the Displacement Coefficient Method and the Coefficient Method. Its basic concept consists in converting the elastic linear displacement of an equivalent Single Degree of Freedom system (SDOF) into a corresponding inelastic displacement. It relies on adequate modifying or reduction coefficient such as the inelastic deformation ratio which is usually developed for systems with known ductility factors ($C_{\mu}$) and ($C_R$) for known yield-strength reduction factor. The present paper proposes a rational approach which estimates this inelastic deformation ratio for SDOF bilinear systems by rigorous nonlinear analysis. It proposes a new inelastic deformation ratio which unifies and combines both $C_{\mu}$ and $C_R$ effects. It is defined by the ratio between the inelastic and elastic maximum lateral displacement demands. Three options are investigated in order to express the inelastic response spectra in terms of: ductility demand, yield strength reduction factor, and inelastic deformation ratio which depends on the period, the post-to-preyield stiffness ratio, the yield strength and the peak ground acceleration. This new inelastic deformation ratio ($C_{\eta}$) is describes the response spectra and is related to the capacity curve (pushover curve): normalized yield strength coefficient (${\eta}$), post-to-preyield stiffness ratio (${\alpha}$), natural period (T), peak ductility factor (${\mu}$), and the yield strength reduction factor ($R_y$). For illustrative purposes, instantaneous ductility demand and yield strength reduction factor for a SDOF system subject to various recorded motions (El-Centro 1940 (N/S), Boumerdes: Algeria 2003). The method accuracy is investigated and compared to classical formulations, for various hysteretic models and values of the normalized yield strength coefficient (${\eta}$), post-to-preyield stiffness ratio (${\alpha}$), and natural period (T). Though the ductility demand and yield strength reduction factor differ greatly for some given T and ${\eta}$ ranges, they remain take close when ${\eta}>1$, whereas they are equal to 1 for periods $T{\geq}1s$.

• Earthquakes and Structures
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• 제13권1호
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• pp.59-66
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• 2017

Modern seismic codes rely on performance-based seismic design methodology which requires that the structures withstand inelastic deformation. Many studies have focused on the inelastic deformation ratio evaluation (ratio between the inelastic and elastic maximum lateral displacement demands) for various inelastic spectra. This paper investigates the inelastic response spectra through the ductility demand ${\mu}$, the yield strength reduction factor $R_y$, and the inelastic deformation ratio. They depend on the vibration period T, the post-to-preyield stiffness ratio ${\alpha}$, the peak ground acceleration (PGA), and the normalized yield strength coefficient ${\eta}$ (ratio of yield strength coefficient divided by the PGA). A new inelastic deformation ratio $C_{\eta}$ is defined; it is related to the capacity curve (pushover curve) through the coefficient (${\eta}$) and the ratio (${\alpha}$) that are used as control parameters. A set of 140 real ground motions is selected. The structures are bilinear inelastic single degree of freedom systems (SDOF). The sensitivity of the resulting inelastic deformation ratio mean values is discussed for different levels of normalized yield strength coefficient. The influence of vibration period T, post-to-preyield stiffness ratio ${\alpha}$, normalized yield strength coefficient ${\eta}$, earthquake magnitude, ruptures distance (i.e., to fault rupture) and site conditions is also investigated. A regression analysis leads to simplified expressions of this inelastic deformation ratio. These simplified equations estimate the inelastic deformation ratio for structures, which is a key parameter for design or evaluation. The results show that, for a given level of normalized yield strength coefficient, these inelastic displacement ratios become non sensitive to none of the rupture distance, the earthquake magnitude or the site class. Furthermore, they show that the post-to-preyield stiffness has a negligible effect on the inelastic deformation ratio if the normalized yield strength coefficient is greater than unity.

• Smart Structures and Systems
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• 제29권1호
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• pp.1-16
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• 2022

Despite recent breakthroughs in deep learning and computer vision fields, the pixel-wise identification of tiny objects in high-resolution images with complex disturbances remains challenging. This study proposes a modified U-net for tiny crack segmentation in real-world steel-box-girder bridges. The modified U-net adopts the common U-net framework and a novel Self-Attention-Self-Adaption (SASA) neuron as the fundamental computing element. The Self-Attention module applies softmax and gate operations to obtain the attention vector. It enables the neuron to focus on the most significant receptive fields when processing large-scale feature maps. The Self-Adaption module consists of a multiplayer perceptron subnet and achieves deeper feature extraction inside a single neuron. For data augmentation, a grid-based crack random elastic deformation (CRED) algorithm is designed to enrich the diversities and irregular shapes of distributed cracks. Grid-based uniform control nodes are first set on both input images and binary labels, random offsets are then employed on these control nodes, and bilinear interpolation is performed for the rest pixels. The proposed SASA neuron and CRED algorithm are simultaneously deployed to train the modified U-net. 200 raw images with a high resolution of 4928 × 3264 are collected, 160 for training and the rest 40 for the test. 512 × 512 patches are generated from the original images by a sliding window with an overlap of 256 as inputs. Results show that the average IoU between the recognized and ground-truth cracks reaches 0.409, which is 29.8% higher than the regular U-net. A five-fold cross-validation study is performed to verify that the proposed method is robust to different training and test images. Ablation experiments further demonstrate the effectiveness of the proposed SASA neuron and CRED algorithm. Promotions of the average IoU individually utilizing the SASA and CRED module add up to the final promotion of the full model, indicating that the SASA and CRED modules contribute to the different stages of model and data in the training process.